Non-causal decision theories are not necessary for A.G.I. design.
I’ll call that and raise you “No decision theory of any kind, causal or otherwise, will either play any important explicit role in, or have any important architectural effect over, the actual design of either the first AGI(s), or any subsequent AGI(s) that aren’t specifically intended to make the point that it’s possible to use decision theory”.
The standard method for training LLM’s is next token prediction with teacher-forcing, penalized by the negative log-loss. This is exactly the right setup to elicit calibrated conditional probabilities, and exactly the “prequential problem” that Solomonoff induction was designed for. I don’t think this was motivated by decision theory, but it definitely makes perfect sense as an approximation to Bayesian inductive inference—the only missing ingredient is acting to optimize a utility function based on this belief distribution. So I think it’s too early to suppose that decision theory won’t play a role.
I’ll call that and raise you “No decision theory of any kind, causal or otherwise, will either play any important explicit role in, or have any important architectural effect over, the actual design of either the first AGI(s), or any subsequent AGI(s) that aren’t specifically intended to make the point that it’s possible to use decision theory”.
The standard method for training LLM’s is next token prediction with teacher-forcing, penalized by the negative log-loss. This is exactly the right setup to elicit calibrated conditional probabilities, and exactly the “prequential problem” that Solomonoff induction was designed for. I don’t think this was motivated by decision theory, but it definitely makes perfect sense as an approximation to Bayesian inductive inference—the only missing ingredient is acting to optimize a utility function based on this belief distribution. So I think it’s too early to suppose that decision theory won’t play a role.